missForest: Nonparametric Missing Value Imputation using Random Forest

The function 'missForest' in this package is used to impute missing values particularly in the case of mixed-type data. It uses a random forest trained on the observed values of a data matrix to predict the missing values. It can be used to impute continuous and/or categorical data including complex interactions and non-linear relations. It yields an out-of-bag (OOB) imputation error estimate without the need of a test set or elaborate cross-validation. It can be run in parallel to save computation time.

Version:	1.5
Imports:	randomForest, foreach, itertools, iterators, doRNG
Suggests:	doParallel
Published:	2022-04-14
DOI:	10.32614/CRAN.package.missForest
Author:	Daniel J. Stekhoven
Maintainer:	Daniel J. Stekhoven <stekhoven at stat.math.ethz.ch>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL:	https://www.r-project.org, https://github.com/stekhoven/missForest
NeedsCompilation:	no
Citation:	missForest citation info
Materials:	README
In views:	MissingData
CRAN checks:	missForest results

Documentation:

Reference manual:	missForest.pdf
Vignettes:	missForest_1.5

Downloads:

Package source:	missForest_1.5.tar.gz
Windows binaries:	r-devel: missForest_1.5.zip, r-release: missForest_1.5.zip, r-oldrel: missForest_1.5.zip
macOS binaries:	r-release (arm64): missForest_1.5.tgz, r-oldrel (arm64): missForest_1.5.tgz, r-release (x86_64): missForest_1.5.tgz, r-oldrel (x86_64): missForest_1.5.tgz
Old sources:	missForest archive

Reverse dependencies:

Reverse depends:	bartMachine, imp4p
Reverse imports:	ADAPTS, funspace, FuzzyImputationTest, GenoPop, highMLR, imanr, KarsTS, longit, MAI, MERO, missCompare, MSPrep, obliqueRSF, pmp, promor, simputation, speaq
Reverse suggests:	CALIBERrfimpute, DepInfeR, hdImpute, MsCoreUtils, mvs, qmtools, tidyLPA

Linking:

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